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1,489 results for “infiltration”
MOD-LSP: MODIS-Based Parameters for Variable Infiltration Capacity (VIC) Model over the Continental US, Mexico, and Southern Canada
<p>The MOD-LSP project contains MODIS-based land and surface (soil and vegetation) parameters for the Variable Infiltration Capacity (VIC) model (Liang et al., 1994), release 5.0 and later (Hamman et al., 2018). The MOD-LSP spatial domain covers the continental United States, Mexico, and southern Canada; the associated domain files can be found in the <a href="https://zenodo.org/record/2564019">PITRI archive</a> (Bohn et al. 2018). This spatial domain and 0.625° (6 km) grid resolution are compatible with the gridded daily meteorological forcings of Livneh et al. (2015) ("L2015" hereafter) (http://ciresgroups.colorado.edu/livneh/data/daily-observational-hydrometeorology-data-set-north-american-extent), which can be disaggregated to hourly time step via the MetSim tool (Bennett et al. 2018) using the <a href="https://zenodo.org/record/2564019">aforementioned PITRI domain files</a> (Bohn et al. 2018).</p> <p>These parameters have two main purposes: (1) to improve upon previous widely-used parameters over the region (e.g., L2015) with updated, higher-resolution land cover maps and spatially explicit observations of surface properties; and (2) to expand from a single parameter set corresponding to one point in time to a series of parameter sets that account for temporal variability at seasonal to decadal scales.</p> <p>A detailed description of methods, the data sources and purposes of different VIC parameter sets within MOD-LSP, and how to use them with VIC, can be found in the MOD-LSP User Guide.pdf, included here. The scripts that were used to create the MOD-LSP parameters are archived on <a href="https://zenodo.org/record/3364149">Zenodo and GitHub</a> (Bohn 2019).</p> <p>If you wish to present or publish results that use these parameter sets, please cite the following paper:</p> <p>Bohn, T. J., and E. R. Vivoni, 2019b: MOD-LSP, MODIS-based land surface properties for assessing land cover variability and change over North America. Sci. Data, 6, 144, doi: 10.1038/s41597-019-0150-2.</p> <p>In addition, if you use the domain files associated with the PITRI precipitation disaggregation to accompany the MOD-LSP parameter files in VIC simulations, please cite the following paper:</p> <p>Bohn, T. J., K. M. Whitney, G. Mascaro, and E. R. Vivoni, 2019: A deterministic approach for approximating the diurnal cycle of precipitation for use in large-scale hydrological modeling. J. Hydrometeorol., 20, 297–317, doi:10.1175/JHM-D-18-0203.1.</p> <p>Contents:</p> <ul> <li>MOD-LSP User Guide v1.0.pdf - Explains how parameters were generated and how to set up the files for input in VIC simulations.</li> <li>global_param.template - Template for global_parameter file, which lists the locations of the other input files and sets various simulation options. The template contains placeholders for some filenames and simulation options, which must be replaced with real values by the user.</li> <li>params.$DOMAIN.L2015.nc - VIC-5 compliant NetCDF parameter files with values taken from the L2015 project for domain $DOMAIN (which is one of "CONUS_MX" or "USMX").</li> <li>params.CONUS_MX.MOD_IGBP.mode.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the CONUS_MX domain, with land cover fractions taken from the MODIS MCD12Q1.006 product and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.2000_2016.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the climatological mean of MODIS observations over the period 2000-2016.</li> <li>params.USMX.NLCD_INEGI.$LCID.$YYYY_$YYYY.nc - VIC-5 compliant NetCDF parameter file over the USMX domain, with land cover fractions taken from the NLCD_INEGI dataset, from year = $LCID, and an annual cycle of land surface properties (LAI, Fcanopy, albedo) derived from the MODIS observations from a single year $YYYY.</li> <li>veg_hist.$DOMAIN.$LCTYPE.$LCID.2000_2016.nc - timeseries of monthly land surface properties (LAI, Fcanopy, albedo) from MODIS observations spanning years 2000-2016, over domain $DOMAIN, aggregated over land cover classification $LCTYPE from year $LCID.</li> </ul>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
ASR01 Short-term assessment of effects of burning on infiltration, runoff, and sediment and nutrient loss on Tallgrass Prairie using rainfall simulation, 1989
Rainfall simulation and overland flow experiments were performed on four plots at a single site on Konza from May to August, 1989. Two plots were treated with a late spring burn and two plots were left unburned. Five simulations were performed on burned plots and three simulatons on unburned plots. Each simulation consisted of a “dry run” followed 24 hours later by a 'wet run'. The dry run consisted of rainfall applied at an intesity of approximately 60 mm/hour. The wet run was the same as a dry run, except when the rainfall was complete, overland flow was applied directly at the top of the plots to simulate run off coming from upslope. Measurements taken include overland flow velocity, water application rate, runoff, hydrograph, water flow depth, sediment content, nitrogen and phosphorus content and percent ground cover (See A.B. Duell, Effects of burning on infiltration, overland flow, and sediment loss on tallgrass prairie, M.S. thesis, Kansas State University, 82pp. for further details).
NanoString dataset for study: Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade
<p>Pre-processed NanoString mRNA abundance data and associated sample sheet for study:</p> <p>Impairment of cancer-associated fibroblasts promotes CD8+ T cell infiltration and enhances sensitivity to immune checkpoint blockade</p>
Single-cell RNA-seq of breast cancer infiltrating T cells (case 1)
<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 2) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads. Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>
Single-cell RNA-seq of breast cancer infiltrating T cells (case 2)
<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 1) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads (this sample was divided into two channels). Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>
Reactive pressure infiltration of Cu-46at.pct. Si into carbon
<p>Raw data linked to the paper published in Acta Materialia :Reactive pressure infiltration of Cu-46at.pct. Si into carbon (DOI:h<a href="https://doi.org/10.1016/j.actamat.2019.07.010">ttps://doi.org/10.1016/j.actamat.2019.07.010</a>)</p>
Infiltration and soil moisture Saugraben and Weiherbach experimental sites
<p><em>Infiltration experiments at Saugraben infiltration experimental site and measured and modelled soil moisture at Weiherbach hydrological experimental catchment</em></p>
Dataset for "Infiltration and resuspension of dilute particle suspensions in micro cavity flow "
<p>This dataset contains the numerical simulation data describing the migration behaviour of particles. Detailed study on the infiltration and resuspension of particle in a cavity flow is presented in a journal paper entitled "Infiltration and resuspension of dilute particle suspensions in micro cavity flow ". We provide all the essential data in a single excel file with the description below:</p> <p> </p> <p>Dataset 1- Particle trajectory for different releasing positions. The parameters are <em>Re</em>=56.9, <em>h</em>=32, <em>l</em>=80. The particle densities are <em>ρ<sub>p</sub></em>=1.3 in plot (Fig. 7a) and <em>ρ<sub>p</sub></em>=2.0 in plot (Fig. 7b). (Data of Fig. 7)</p> <p>Dataset 2 - Particle trajectory for different periodic domain lengths. The parameters are <em>ρ<sub>p</sub></em>=1.3, <em>Re</em>=56.9, <em>h</em>=32 and <em>l</em>=80. (Data of Fig. 8)</p> <p>Dataset 3 - The time evolution of (Fig. 9a) the vertical position <em>z</em>, (Fig. 9b) the horizontal velocity <em>u<sub>x</sub></em>, and (Fig. 9c) the vertical velocity <em>u<sub>z</sub></em> of a single particle with the same Renolds number <em>Re</em>=56.9 and cavity size of <em>h</em>=32 and <em>l</em>=80, but different particle densities. (Data of Fig. 9)</p> <p>Dataset 4 - The trajectories of a single particle over the cavity with: (Fig. 10a) different particle densities but the same Renolds number <em>Re</em>=85.3 and cavity size of <em>h</em>=16 and <em>l</em>=80; (Fig. 10b) different Reynolds numbers but the same particle density <em>ρ<sub>p</sub></em>=1.3 and cavity size of <em>h</em>=32 and <em>l</em>=80; (Fig. 10c) different cavity sizes but the same Renolds number<em> Re</em>=56.9 and particle density <em>ρ<sub>p</sub></em>=1.3. The dashed lines represent the boundary of the cavity. (Data of Fig. 10)</p> <p>Dataset 5 - Typical particle trajectories in the cavity. The dashed lines represent the boundary of the trap. (Data of Fig. 12)</p> <p>Dataset 6 - The variation of trap efficiency with particle density for (Fig. 13a) different cavity sizes with the same <em>Re</em>=56.9, and (Fig. 13b) different Reynolds numbers with the same cavity size <em>h</em>=32, <em>l</em>=80, respectively. (Data of Fig. 13)</p> <p>Dataset 7 - The critical entering distance as a function of the scaling parameter <em>T<sub>h</sub></em>. The fitting line is performed with an exponential function with the same form as Eq. (19). (Data of Fig.14)</p> <p>Dataset 8 - The variation of the trap efficiency with the trap number. (Data of Fig.15)</p> <p> </p>
Immune repertoire profiling reveals that clonally expanded B and T cells infiltrating diseased human kidneys can also be tracked in the blood
<p>Recent advances in high-throughput sequencing allow for the competitive analysis of the human B and T cell immune repertoire. In this study we compared Immunoglobulin and T cell receptor repertoires of lymphocytes found in kidney and blood samples of 10 patients with various renal diseases based on next-generation sequencing data.</p>
Prosthetic Sockets: Tensile Behavior of Vacuum Infiltrated Fused Deposition Modeling Sandwich Structure Composites
<p>The development of novel materials will enable a new generation of prosthetic devices to be built with additive manufacturing (AM). Vacuum infiltrated sandwich structure composites are a promising approach for building prosthetic sockets via AM. In this dataset, we test the tensile properties of 18 different composite material configurations using ASTM D638. These composites were manufactured using a custom vacuum infiltration method and had varying filament materials, infiltrated matrix materials, and print directions. Several material-matrix-print composites showed higher ultimate tensile strengths and reduced anisotropy compared to full-infill control samples. However, the mechanical properties of these composites were limited by a large degree of porosity due to the manufacturing method. Still, the results were promising enough to create a proof of concept prosthetic socket via the vacuum infiltration method. Future research should focus on reducing porosity defects and investigating additional material-matrix-print combinations.</p>
Dataset for tumor infiltrating lymphocyte classification (304,097 image patches from TCGA)
<p>This is a dataset of images with or without tumor-infiltrating lymphocytes (TILs). The original images are from Abousamra et al. (2022) and Saltz et al. (2018), and the original whole slide images are from TCGA. This dataset is a subset of the data presented in Abousamra et al. (2022) (with new data partitions).</p> <p>If you use this dataset, please cite the following papers, as well as this Zenodo page.</p> <p>Abousamra, S., Gupta, M. D., Hou, L., Batiste, R., Zhao, T., Shankar, A., Rao, A., Chen, C., Samaras, D., Kurc, T., & Saltz, J. (2022). Deep Learning-Based Mapping of Tumor Infiltrating Lymphocytes in Whole Slide Images of 23 Types of Cancer. <em>Frontiers in Oncology</em>, 5971. https://doi.org/10.3389/fonc.2021.806603</p> <p>Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., & Danilova, L. (2018). Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. <em>Cell Reports</em>, <em>23</em>(1), 181-193.</p> <p> </p> <p>The acknowledgements from the <em>Frontiers in Oncology</em> and <em>Cell Reports</em> papers are included below:</p> <blockquote> <p>This work was supported by the National Institutes of Health (NIH) and National Cancer Institute (NCI) grants UH3-CA22502103, U24-CA21510904, 1U24CA180924-01A1, 3U24CA215109-02, and 1UG3CA225021-01 as well as generous private support from Bob Beals and Betsy Barton. AR and AS were partially supported by NCI grant R37-CA214955 (to AR), the University of Michigan (U-M) institutional research funds and also supported by ACS grant RSG-16-005-01 (to AR). AS was supported by the Biomedical Informatics & Data Science Training Grant (T32GM141746). This work was enabled by computational resources supported by National Science Foundation grant number ACI-1548562, providing access to the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center, and also a DOE INCITE award joint with the MENNDL team at the Oak Ridge National Laboratory, providing access to Summit high performance computing system. The funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.</p> </blockquote> <p> </p> <blockquote> <p>We are grateful to all the patients and families who contributed to this study. Funding from the Cancer Research Institute is gratefully acknowledged, as is support from National Cancer Institute (NCI) through U54 HG003273, U54 HG003067, U54 HG003079, U24 CA143799, U24 CA143835, U24 CA143840, U24 CA143843, U24 CA143845,U24 CA143848, U24 CA143858, U24 CA143866, U24 CA143867, U24 CA143882, U24 CA143883, U24 CA144025, P30 CA016672, U24CA180924, U24CA210950, U24CA215109, NCI Contract HHSN261201400007C, and Leidos Biomedical Contract 14X138. A.U.K.R. and P.S were supported by CCSG Bioinformatics Shared Resource P30 CA01667, ITCR U24 Supplement 1U24CA199461-01, a gift from Agilent technologies, CPRIT RP150578, and a Research Scholar Grant from the American Cancer Society (RSG-16-005-01). This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation XSEDE Science Gateways program under grant ACI-1548562 allocation TG-ASC130023. The authors would like to thank Stony Brook Research Computing and Cyberinfrastructure and the Institute for Advanced Computational Science at Stony Brook University for access to the high-performance LIred and SeaWulf computing systems, the latter of which was supported by National Science Foundation grant (#1531492).</p> </blockquote> <p>------------------------------------</p> <p>This dataset includes 304,097 image patches. All images are 100 x 100 pixels at 0.5 micrometers per pixel. An image is TIL-positive if there are at least two TILs present.</p> <p>Refer to `images-tcga-tils-metadata.csv` for information about each image. That spreadsheet has the following columns:</p> <pre><code>partition,study,barcode,label,path,md5</code></pre> <p>Partition specifies which partition the image is part of (train, val, test). Study is the TCGA study the image is part of (e.g., acc for TCGA-ACC). Barcode is the TCGA participant barcode. This is used during partitioning, to ensure that images from the same participant are not present in different data partitions. Label is either til-negative or til-positive. An image is til-positive if there are at least two TILs in the image. Path is the path to the PNG image. All images are stored as PNG. Md5 is the md5 hash of the image. This can be used to ensure there are no duplicate images and to verify the integrity of images.</p> <p>There are study-specific directories in the directory `images-tcga-tils`, and there is a directory named `pancancer` that includes images from all the included TCGA studies. That directory uses symlinks to avoid storing duplicate data.</p> <p> </p>
Flower production and water infiltration in LandKlif experimental urban grassland plots
<p><span>Mixtures of grassland communities (three different compositions varying in composition of grasses and forbs) were sown in field experimental areas (56 plots, 2 x 4 m each) in Munich and Weihenstephan in 2020. In June and August 2021, flower production of dicotyledonous, richness of dicotyledonous species in bloom, and soil water infiltration were measured in the plots, as a proxy to ecosystem functions related to resource offer to pollinators and water regulation. For integrity of the database, all field experimental units of this study are associated to Plot ID 7835_1_U, but the variable PlotID is deprecated. </span></p> <p><span>LandKlif is funded by the Bavarian State Ministry of Science and the Arts within the Bavarian Climate Research Network (bayklif). Within the five year funding period of bayklif, five interdisciplinary senior research associations and five junior research groups are be financed with a total sum of 18 million Euro. LandKliF, as one of the five interdisciplinary senior research associations, addresses the effects of climate change on biodiversity and ecosystem services in semi-natural, agricultural and urban landscapes.</span></p>
Data and results for manuscript "Imaging groundwater infiltration dynamics in karst vadose zone with long-term ERT monitoring"
<p>This data set contains raw and inverted data from an Electrical Resistivity Tomography (ERT) monitoring experiment conducted over a period of three years at the Rochefort Cave Laboratory (RCL) site in South Belgium. It highlights variable hydrodynamics in the karst vadose zone of Lorette Cave. More conventional hydrological measurements (drip discharge monitoring, soil moisture and water conductivity data sets) are also included in the package, which aims at provide a thorough understanding of the groundwater infiltration. Seasonal changes affect all the imaged areas leading to increases in resistivity in spring/summer attributed to enhanced evapotranspiration, whereas winter is characterised by a general decrease in resistivity associated with a groundwater recharge of the vadose zone. This study provides detailed images of the sources of drip discharge spots traditionally monitored in caves and aims to support modelling approaches of karst hydrological processes.</p>
Infiltration campaign in the Mercier sub-catchment (Yzeron catchment, periphery of Lyon France)
<p>This study was part of the AVuPUR project (Assessing the Vulnerability of PeriUrban catchments, 2008-2011), dedicated to the observation and understanding of the impact of urbanization on the hydrological cycle in periurban catchments. We provide the results of a field campaign conducted in the Mercier catchment, a sub-catchment of the Yzeron catchment located in the Monts du Lyonnais, in the western part of Lyon city, France. The campaign was dedicated to the characterization of top soil hydraulic properties. Some data are also available at -15 and -30 cm depth.<br> The data set includes 66 sampled points with information of particle size data, dry bulk density and porosity, hydraulic conductivity at saturation and close to saturation for 46 measurement points.<br> We also provide estimates of the parameters of the retention and hydraulic conductivity curves for the various sample points.</p> <p>Sample points were selected in the Mercier catchment (6.7 km2) by combining information about geology, land use and pedology. Two types of infiltration experiments were conducted: single ring infiltration tests with a positive pressure at the soil surface (3 replicates)(Beerkan method)_ and mini-disk infiltrometers using a -20 mm suction (0 to 3 replicates).</p>
Data used in analyses of Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells"
<p>CosMx 1000-plex data and R code from childhood-onset lupus nephris samples, generated for the article Danaher et al. 2024 "Childhood-onset lupus nephritis is characterized by complex interactions between kidney stroma and infiltrating immune cells".</p>
Data of soil infiltration tests and soil samples, Los Arenales MAR Systems, Santiuste and La Laguna del Señor infiltration basins
<p><span>Infiltration test and soil sample data utilised in the article "a nature-based solution to enhance aquifer recharge: combining trees and infiltration basins"</span></p>
VICGlobal: soil and vegetation parameters for the Variable Infiltration Capacity hydrological model
<p>## VICGlobal: soil, vegetation, and elevation band input files for the VIC hydrological model</p> <p>Date updated: June 28, 2021</p> <p>Authors and affiliations: Jacob Schaperow (1), Dongyue Li (1,2)<br> 1. Department of Civil and Environmental Engineering, UCLA<br> 2. Department of Geography, UCLA<br> Author contact info: jschap@g.ucla.edu</p> <p>The current version, v1.6d improves upon v1.6c by splitting the image parameters by continent, reducing file sizes.</p> <p>v1.6c is the same as v1.6, except that the image mode parameters have been updated to reflect the changes made to the classic mode parameters (e.g. r0 and rmin are different, and albedo, fcanopy, and LAI are calculated based on snow-free values).</p> <p>## Overview</p> <p>VICGlobal is a dataset that can be used to run the Variable Infiltration Capacity (VIC) hydrological model over regional to continental scales. The dataset is at 1/16 degree resolution and has latitudinal coverage from -60 to 85 degrees. All files are referenced to the WGS84 ellipsoid and datum (EPSG code 4326).</p> <p>The vegetation parameter file uses the IGBP classification and use partial land use types. The vegetation parameter rooting depths and root fractions are based on the method of Zeng (2001). The vegetation library file is largely the same as that of Livneh et al. (2013; 2015); however, the monthly average LAI, canopy fraction, and albedo values for each land cover type are calculated based on MODIS observations from 2017, using the method of Bohn and Vivoni (2019).</p> <p>There are two vegetation libraries: one for the northern hemisphere, and one for the southern hemisphere, in order to account for the seasonality of LAI, canopy fraction, and albedo.</p> <p>WARNING: although it appears small in compressed form, the image driver parameter input file, VICGlobal_params.nc, is about 140 GB when unzipped. Users are encouraged to use the image mode parameters that are already split by continent. For example, the parameter file for Africa is about 19 GB.</p> <p>A data descriptor is in preparation for submission to Nature Scientific Data (https://www.nature.com/sdata/).</p> <p>Other VIC input datasets (coverage limited to North America):<br> * Bohn and Vivoni MOD-LSP dataset: https://zenodo.org/record/2559631</p> <p>## List of contents</p> <p>Inputs for VIC-4 or the VIC-5 Classic Driver<br> * Soil parameter file<br> * Vegetation parameter file<br> * Elevation band file<br> * Vegetation library files (one each for the northern and southern hemispheres)</p> <p>Inputs for the VIC-5 Image Driver<br> * Parameter file (global)<br> * Domain file (global)<br> * Parameter files for each continent<br> * Africa<br> * Australia<br> * Eurasia (except Kamchatka)<br> * Kamchatka<br> * North America<br> * Oceania (New Zealand and nearby islands)<br> * South America<br> * Domain files for each continent<br> * GeoTiffs with continent masks</p> <p>Matlab codes for subsetting the VICGlobal parameters to a region of interest are also provided.</p> <p>## References</p> <p>* Bohn and Vivoni (2019). MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change. https://www.nature.com/articles/s41597-019-0150-2</p> <p>* Livneh et al. (2015). A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada 1950–2013. https://www.nature.com/articles/sdata201542</p> <p>* Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis, K. M., Maurer, E. P. and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: Update and extensions, J. Clim., 26(23), 9384–9392, doi:10.1175/JCLI-D-12-00508.1, 2013.</p> <p>* Zeng (2001). Global Vegetation Root Distribution for Land Modeling. Journal of Hydrometeorology. https://doi.org/10.1175/1525-7541(2001)002<0525:GVRDFL>2.0.CO;2</p>
Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data
<p>Processed Datasets for:</p> <p>EGA Study ID: EGAS00001006141</p> <p>EGA Dataset ID: EGAD00001008662</p> <p> </p> <p>Find processed files and arrow files</p> <p> </p> <p>Abstract:</p> <p>Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8<sup>+</sup> TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.</p>
Nitrogen cycling, soil properties and infiltration rates along a topographic gradient in lawns in Baltimore County, Maryland
The aim of this research was to examine how topography and homeowner fertilizer practices affected soil and hydrologic properties of residential lawns to determine if there are locations within lawns that have the potential to act as hotspots of nitrogen transport during rain events. This data set contains measurements of saturated infiltration rates, sorptivity, soil moisture, soil organic matter, pH, soil nitrate, soil ammonium, denitrification potentials and limiting factors, and nitrogen mineralization rates from fertilized and unfertilized residential and institutional lawns. Study lawns were located at homes of people who agreed to volunteer their lawn for the study from a door knocking campaign. Four sampling houses were located in an exurban neighborhood in Baisman Run. Five sampling houses were located in a suburban neighborhood in Dead Run. Two sampling locations on institutional lawns were located at University of Maryland Baltimore County. At the exurban study houses and institutional lawns sites,we identified one hillslope to conduct sampling on. At the Dead Run houses we identified one hillslope on the front yard and one in the backyard as there were distinct locations that were not present in the exurban neighborhood. In total we sampled on 16 hillslopes. At each hillslope, we identified the top, toe and swale locations. At each hillslope location, we selected three sampling locations along a transect (maximum 10 meters in length; total of 144 sampling locations). At each sampling location we ran a Cornell Sprinkle Infiltrometer to measure sorptivity and saturated infiltration rates. Volumetric water content was measured before and after infiltrometer runs with a Field Scout TDR 300 with 7.5 cm rods. In addition, at each sampling location we took two soil cores to 10 cm depth, and combined and homogenized the two cores for that sampling location for a total of 144 soil samples. Soil cores were stored on ice in the field, and then stored at 4°C in the
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.